--- /dev/null
+/*
+ * Created on Jul 15, 2007
+ *
+ * Copyright (c) 2007, the JUNG Project and the Regents of the University
+ * of California
+ * All rights reserved.
+ *
+ * This software is open-source under the BSD license; see either
+ * "license.txt" or
+ * http://jung.sourceforge.net/license.txt for a description.
+ */
+package edu.uci.ics.jung.algorithms.scoring;
+
+import edu.uci.ics.jung.algorithms.scoring.util.ScoringUtils;
+import edu.uci.ics.jung.graph.Graph;
+
+import org.apache.commons.collections15.Transformer;
+
+/**
+ * Assigns hub and authority scores to each vertex depending on the topology of
+ * the network. The essential idea is that a vertex is a hub to the extent
+ * that it links to authoritative vertices, and is an authority to the extent
+ * that it links to 'hub' vertices.
+ *
+ * <p>The classic HITS algorithm essentially proceeds as follows:
+ * <pre>
+ * assign equal initial hub and authority values to each vertex
+ * repeat
+ * for each vertex w:
+ * w.hub = sum over successors x of x.authority
+ * w.authority = sum over predecessors v of v.hub
+ * normalize hub and authority scores so that the sum of the squares of each = 1
+ * until scores converge
+ * </pre>
+ *
+ * HITS is somewhat different from random walk/eigenvector-based algorithms
+ * such as PageRank in that:
+ * <ul>
+ * <li/>there are two mutually recursive scores being calculated, rather than
+ * a single value
+ * <li/>the edge weights are effectively all 1, i.e., they can't be interpreted
+ * as transition probabilities. This means that the more inlinks and outlinks
+ * that a vertex has, the better, since adding an inlink (or outlink) does
+ * not dilute the influence of the other inlinks (or outlinks) as in
+ * random walk-based algorithms.
+ * <li/>the scores cannot be interpreted as posterior probabilities (due to the different
+ * normalization)
+ * </ul>
+ *
+ * This implementation has the classic behavior by default. However, it has
+ * been generalized somewhat so that it can act in a more "PageRank-like" fashion:
+ * <ul>
+ * <li/>this implementation has an optional 'random jump probability' parameter analogous
+ * to the 'alpha' parameter used by PageRank. Varying this value between 0 and 1
+ * allows the user to vary between the classic HITS behavior and one in which the
+ * scores are smoothed to a uniform distribution.
+ * The default value for this parameter is 0 (no random jumps possible).
+ * <li/>the edge weights can be set to anything the user likes, and in
+ * particular they can be set up (e.g. using <code>UniformDegreeWeight</code>)
+ * so that the weights of the relevant edges incident to a vertex sum to 1.
+ * <li/>The vertex score normalization has been factored into its own method
+ * so that it can be overridden by a subclass. Thus, for example,
+ * since the vertices' values are set to sum to 1 initially, if the weights of the
+ * relevant edges incident to a vertex sum to 1, then the vertices' values
+ * will continue to sum to 1 if the "sum-of-squares" normalization code
+ * is overridden to a no-op. (Other normalization methods may also be employed.)
+ * </ul>
+ *
+ * @param <V> the vertex type
+ * @param <E> the edge type
+ *
+ * @see "'Authoritative sources in a hyperlinked environment' by Jon Kleinberg, 1997"
+ */
+public class HITS<V,E> extends HITSWithPriors<V,E>
+{
+
+ /**
+ * Creates an instance for the specified graph, edge weights, and alpha
+ * (random jump probability) parameter.
+ * @param g the input graph
+ * @param edge_weights the weights to use for each edge
+ * @param alpha the probability of a hub giving some authority to all vertices,
+ * and of an authority increasing the score of all hubs (not just those connected
+ * via links)
+ */
+ public HITS(Graph<V,E> g, Transformer<E, Double> edge_weights, double alpha)
+ {
+ super(g, edge_weights, ScoringUtils.getHITSUniformRootPrior(g.getVertices()), alpha);
+ }
+
+ /**
+ * Creates an instance for the specified graph and alpha (random jump probability)
+ * parameter. The edge weights are all set to 1.
+ * @param g the input graph
+ * @param alpha the probability of a hub giving some authority to all vertices,
+ * and of an authority increasing the score of all hubs (not just those connected
+ * via links)
+ */
+ public HITS(Graph<V,E> g, double alpha)
+ {
+ super(g, ScoringUtils.getHITSUniformRootPrior(g.getVertices()), alpha);
+ }
+
+ /**
+ * Creates an instance for the specified graph. The edge weights are all set to 1
+ * and alpha is set to 0.
+ * @param g the input graph
+ */
+ public HITS(Graph<V,E> g)
+ {
+ this(g, 0.0);
+ }
+
+
+ /**
+ * Maintains hub and authority score information for a vertex.
+ */
+ public static class Scores
+ {
+ /**
+ * The hub score for a vertex.
+ */
+ public double hub;
+
+ /**
+ * The authority score for a vertex.
+ */
+ public double authority;
+
+ /**
+ * Creates an instance with the specified hub and authority score.
+ */
+ public Scores(double hub, double authority)
+ {
+ this.hub = hub;
+ this.authority = authority;
+ }
+
+ @Override
+ public String toString()
+ {
+ return String.format("[h:%.4f,a:%.4f]", this.hub, this.authority);
+ }
+ }
+}
Code Review / controller.git / blobdiff